Post: Ben Shneiderman’s 2018 interview offers a compelling mix of optimism and realism about artificial intelligence. One part that stuck with me was his deep dive into the failure of Google Flu Trends—a classic case of AI hype outrunning reality. In the early 2010s, Google tried to predict flu outbreaks using search data. At first, it looked promising—AI spotting trends faster than hospitals. But over time, the algorithm started missing the mark. Shneiderman described this as a case of algorithmic hubris—the false confidence that more data and smarter models automatically mean better predictions.
This feels especially relevant today. In 2025, we see AI used in everything from financial forecasting to disease modeling. But the problem remains: if we don’t fully understand what’s going on inside the black box—or if the world shifts beneath our data—we’re at risk of being led astray. Shneiderman’s takeaway? Transparency, explainability, and human oversightare non-negotiable. AI can be powerful, but it’s not infallible—and pretending otherwise is where the real danger begins.